Big or Small, Everyone Is Asking: Where Do We Start With AI?

Last week at NADA, I found myself walking between two completely different conversations.

In one room, I was speaking with senior leaders from some of the largest automotive manufacturers in the world about their AI strategy.

A few minutes later, I was talking with the owner of a small local dealership trying to figure out how AI fits into their business.

Different scale. Different resources. Completely different environments.

And yet, the questions were almost identical.

Where do we start?
How do we cut through all the noise?
How do we use AI in a way that actually drives profit?

It struck me how little company size changes the fundamental challenge.

The Same Questions, Regardless of Scale

There’s a common assumption that large enterprises and small businesses approach AI differently. In practice, their concerns sound remarkably similar.

The dealership owner feels urgency. They worry about being left behind, about competitors moving faster, about investing in the wrong tool.

The enterprise executive faces complexity. Layers of systems, competing priorities, organizational silos.

But underneath both perspectives is the same uncertainty: what does AI actually mean for this business?

Everyone is being told that AI is the answer. Few people feel confident they’ve clearly defined the question.

The Overwhelming Landscape of “Solutions”

Walking the show floor made this even more obvious.

Hundreds of vendors. Every booth promising transformation. AI attached to almost every product description.

Some solutions were genuinely innovative. Others felt like traditional tools rebranded with new terminology.

For leaders trying to make decisions, it becomes overwhelming quickly.

When everything claims to be the solution, it becomes harder to identify the real problem.

That’s where many organizations get stuck — not because they lack interest, but because they start evaluating tools before understanding what needs to change.

Starting With the Problem, Not the Technology

The most effective conversations I had last week didn’t begin with AI capabilities. They began with operational friction.

Where are teams spending hours on manual work?
Where are decisions slow because information is scattered?
Where are opportunities missed because data isn’t connected?

Those questions cut through the noise.

Whether the organization is massive or small, the starting point tends to look the same:

Identify the expensive manual processes.
Find where time and money are leaking.
Look for revenue opportunities hiding in existing workflows.

AI becomes valuable when it targets something specific — not when it’s introduced as a general initiative.

Why Company Size Doesn’t Change the Path

It’s easy to assume that enterprises need large-scale AI transformation while smaller organizations should focus on simpler automation.

But the reality is more nuanced.

Both groups need early wins.

The dealership owner needs momentum to justify investment and prove value quickly. The enterprise needs momentum to align stakeholders and build internal support.

In both cases, solving one meaningful problem often matters more than building a comprehensive strategy upfront.

Starting small isn’t a limitation. It’s a practical approach.

Finding the First Problem Worth Solving

The companies that make progress usually follow a similar path.

They don’t chase every opportunity. They choose one.

Often it’s something operational:

A manual reporting process that consumes hours every week.
Lead prioritization that relies entirely on guesswork.
Inventory decisions made without predictive insight.
Customer signals buried across disconnected systems.

Solving one of these problems well creates clarity. Teams see what AI actually looks like inside their environment. Resistance decreases. Confidence grows.

And from there, expansion becomes easier.

Avoiding the “Shiny Object” Trap

One of the biggest risks right now is jumping straight into tools because they look impressive.

AI is evolving quickly, and new solutions appear almost daily. But technology without context rarely produces lasting results.

The better approach is slower at the beginning but faster in the long run:

Define the outcome first.
Understand the process.
Then choose the technology that supports it.

That sequence sounds obvious, but it’s surprisingly easy to reverse when the market is filled with promises.

Momentum Builds From Real Outcomes

Once organizations see a tangible improvement — reduced manual work, clearer insights, measurable financial impact — the conversation changes.

AI stops feeling experimental.

It becomes part of how the business operates.

And interestingly, the teams that succeed don’t necessarily move faster. They move more intentionally.

They focus on impact instead of chasing trends.

Learning From Others’ Experiences

Another insight from conversations at the event was how valuable shared experiences can be. Hearing how other organizations navigated similar challenges helped leaders rethink their own starting point.

If you’re interested in how client experiences influence AI execution and shape real-world outcomes, our separate dedicated post might offer additional insight.

Where to Begin — A Practical Perspective

If there’s one consistent takeaway, it’s this:

The size of the company doesn’t change the starting point.

Both the local dealership owner and the global manufacturer are trying to answer the same question: what problem is worth solving first?

AI success rarely begins with a grand vision. It starts with a clear problem and a measurable goal.

Organizations that begin there tend to build momentum naturally.

If you’re exploring practical ways to translate AI into operational impact, you can learn more about our approach.

Final Thought

Everyone is hearing that AI matters. That part is no longer in question.

What’s harder is deciding where to begin when everything feels urgent.

The answer is usually simpler than expected.

Find the place where work feels heavy, decisions feel slow, or opportunities feel hidden.

Start there.

Because whether you’re running a global enterprise or a small local business, the path forward tends to look surprisingly similar.